Summary Approximately 30% of colorectal cancer (CRC) risk is due to genetic (inherited) factors. Using mouse models, epistatic effects (risks observed only in the presence of a second genetic variant) and synergistic effects (multiplicative effects between genetic variants) have been shown to be important determinants of cancer risk. Identification of epistatic and synergistic interactions using genotypes from whole genome association studies is difficult due to the large number of possible combinations. We propose a mouse- human strategy to target regions in the human genome for genetic interaction analyses to reduce the complexity of these studies. Data from human case/control studies show that variants in two genes, AURKA and PTPRJ, increase CRC risk. In the mouse, both genes map to loci (genetic regions) that interact with other loci to synergistically increase cancer risk. The goal of this proposal is to identify susceptibility variants that interact with AURKA and PTPRJ to increase CRC risk. We hypothesize that the human equivalent loci to mouse CRC susceptibility loci will interact with AURKA and PTPRJ. To test this hypothesis and to identify interacting genetic variants for CRC risk we will: 1. Test variants from coding and regulatory regions of AURKA- and PTPRJ-interacting loci for variant specific changes in tumors. Previous studies show that cancer susceptibility variants are preferentially gained and cancer resistance variants are preferentially lost in tumors, thus providing a tool to identify these variants. Using matched normal and CRC tumor DNA from 600 individuals, variants that map to candidate AURKA and PTPRJ- interacting loci will be assessed for variant specific gains or losses. 2. Conduct two-way interaction studies of interacting CRC loci identified from mouse models. Variants that map to human equivalent regions of four interacting mouse susceptibility loci will be tested for genetic interactions in humans using published whole genome association data from 2200 CRC cases and controls. Significant findings from Aims 1 and 2 will be validated by sequence and gene expression studies in the strains of mice used to map the susceptibility loci. Variants showing evidence of cancer risk will be the focus of future population-based case control studies and mechanistic studies. This work will lead to the identification of interacting genetic variants which increase CRC risk and will result in better risk assessment tools for CRC. Since cancer mortality for CRC can be significantly reduced by the removal of precursor polyps during screening colonoscopy, identification of at risk individuals will decrease the incidence and mortality of this disease. Genes and pathways identified from these studies will provide new therapeutic targets for CRC treatment.